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Alyssa Shanice

Alyssa Shanice

AI Training Specialist | RLHF, NLP Annotation & LLM Evaluation

UNITED_KINGDOM flag
London, United Kingdom
$85.00/hrExpertOtherAppenMercor

Key Skills

Software

Other
AppenAppen
MercorMercor
Micro1
OneFormaOneForma
Anno-MageAnno-Mage
HiveMindHiveMind
Kili TechnologyKili Technology

Top Subject Matter

Large Language Models
Natural Language Processing

Top Data Types

TextText
VideoVideo
Computer Code ProgrammingComputer Code Programming

Top Task Types

RLHF
Evaluation Rating
Question Answering
Classification
Text Generation
Text Summarization

Freelancer Overview

I am an AI Training and Data Annotation Specialist with hands-on experience improving machine learning systems through high-quality data labeling, prompt evaluation, and model response assessment. My work focuses on evaluating AI outputs for accuracy, reasoning quality, safety, and alignment with human intent. I have contributed to projects involving text annotation, conversational AI evaluation, search relevance grading, and transcription quality assessment, where precision, consistency, and adherence to strict guidelines are essential. I bring a strong analytical mindset and attention to detail when reviewing datasets and training signals used to improve large language models. My experience includes prompt engineering evaluation, classification tasks, ranking model responses, and identifying edge cases that help strengthen model performance. I am particularly skilled at interpreting complex annotation guidelines, maintaining high accuracy across large volumes of tasks, and ensuring training data meets the quality standards required for production AI systems.

ExpertEnglish

Labeling Experience

Content Categorization and Dataset Quality Review Data Type:

TextEntity Ner Classification
Performed entity recognition and categorization tasks within structured text datasets. Identified and labeled named entities such as people, locations, organizations, and key terms according to annotation guidelines. Reviewed datasets for labeling consistency and flagged ambiguous entries to maintain high data quality for machine learning training pipelines.

Performed entity recognition and categorization tasks within structured text datasets. Identified and labeled named entities such as people, locations, organizations, and key terms according to annotation guidelines. Reviewed datasets for labeling consistency and flagged ambiguous entries to maintain high data quality for machine learning training pipelines.

2025 - Present

Conversational AI Dialogue Annotation

TextRLHF
Annotated conversational datasets used for training dialogue systems and large language models. Evaluated AI responses for relevance, helpfulness, and alignment with user intent. Provided preference rankings and feedback signals that help guide reinforcement learning from human feedback (RLHF) processes used to improve model behavior and response quality.

Annotated conversational datasets used for training dialogue systems and large language models. Evaluated AI responses for relevance, helpfulness, and alignment with user intent. Provided preference rankings and feedback signals that help guide reinforcement learning from human feedback (RLHF) processes used to improve model behavior and response quality.

2023 - Present

Speech and Transcription Data Annotation

AudioTranscription
Reviewed speech datasets and verified transcription accuracy for machine learning training pipelines. Corrected misheard words, labeled speaker segments, and ensured transcripts followed formatting guidelines. Maintained high accuracy while working across large volumes of audio recordings used to train speech-to-text models.

Reviewed speech datasets and verified transcription accuracy for machine learning training pipelines. Corrected misheard words, labeled speaker segments, and ensured transcripts followed formatting guidelines. Maintained high accuracy while working across large volumes of audio recordings used to train speech-to-text models.

2023 - 2023

Search Relevance and Query Intent Evaluation

TextEvaluation Rating
Reviewed search queries and ranked results to evaluate how well returned results matched user intent. Assigned relevance ratings based on usefulness, accuracy, and contextual alignment with the query. Flagged misleading or low-quality results and ensured labeled data followed strict relevance grading frameworks used to improve search ranking algorithms.

Reviewed search queries and ranked results to evaluate how well returned results matched user intent. Assigned relevance ratings based on usefulness, accuracy, and contextual alignment with the query. Flagged misleading or low-quality results and ensured labeled data followed strict relevance grading frameworks used to improve search ranking algorithms.

2022 - 2022

Text Classification and Intent Annotation

TextClassification
Annotated large datasets of text used for training natural language processing models. Tasks included categorizing sentences by topic, labeling sentiment, and identifying user intent within conversational prompts. Applied detailed annotation guidelines to ensure consistency and accuracy across high-volume datasets. The labeled data contributed to improving language model understanding of intent, context, and conversational structure.

Annotated large datasets of text used for training natural language processing models. Tasks included categorizing sentences by topic, labeling sentiment, and identifying user intent within conversational prompts. Applied detailed annotation guidelines to ensure consistency and accuracy across high-volume datasets. The labeled data contributed to improving language model understanding of intent, context, and conversational structure.

2020 - 2021

Education

U

University of London

Bachelor of Science, Computer Science

Bachelor of Science
2023

Work History

I

Independent Development Project

AI Platform Developer

London
2025 - Present
P

Personal Development & Research Projects

Software Engineer (AI Systems)

London
2023 - Present